Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantifying Biopolymer Sequence Recognition using Biophysically Informed Machine Learning

ABSTRACT: Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene expression regulation and signal transduction. Given their importance, next-generation sequencing (NGS) based assays that characterize such recognition with high-throughput are increasingly being used to profile a range of protein classes and interactions. However, these methods do not measure the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to quantify sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from \textit{in vivo} data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it quantifies the absolute affinity of protein-ligand interactions. Its applicability extends beyond thermodynamic equilibrium binding, to the kinetics of kinase-substrate interactions. Altogether, ProBound provides a versatile algorithmic framework for understanding sequence recognition in a wide variety of biological contexts.

ORGANISM(S): Homo sapiens Drosophila melanogaster

PROVIDER: GSE175942 | GEO | 2021/06/02

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Synthetic genetic circuit reveals overlapping binding sites underlie TF genomic occupancy

Project description:Sequence-specific DNA binding by transcription factors (TFs) is required for accurately regulating target gene expression. However, rules governing TF-DNA interactions remain elusive because current high-throughput approaches to map them fail to identify lower-affinity interactions. To address this gap, we have developed PADIT-seq, a novel technology to detect TF-DNA interactions across the entire spectrum of affinities. By profiling the binding preferences of Egr1 and Hoxd13 to all possible 10-mers, we found that nucleotides flanking high-affinity binding sites create additional, overlapping lower-affinity sites that together modulate TF genomic occupancy. The formation of these extended recognition sequences is enabled by an inherent property of TF binding sites to interweave together. Highlighting the importance of lower-affinity binding sites, we provide mechanistic insights into their role in fine-tuning gene regulation.

2024-03-06 | GSE250601 | GEO

DNA G-quadruplexes are transcription factor binding hot spots in human chromatin

Project description:Gene expression is governed by transcription factors (TFs) that commonly recognize short DNA sequence motifs. However, TF recruitment to genomic target sites in chromatin is complex and many parameters that ultimately dictate binding-site specificity are still unknown. Here, we systematically explore the interaction of TFs with synthetic and endogenous G-quadruplexes (G4s) DNA secondary structures. Our findings reveal that certain TFs are directly recruited to promoters by endogenous G4s with binding affinities comparable to canonical B-DNA binding and that endogenous G4-TF interactions can be disrupted with synthetic small molecule ligands. G4s in promoters are bound by a large number of TFs and associated with high transcriptional activity. This discovery of structure-specific recognition expands our understanding of how TFs regulate gene transcription.

2021-03-24 | GSE145090 | GEO

Native holdup (nHU) to measure binding affinities from cell extracts

Project description:Characterizing macromolecular interactions is essential for understanding cellular processes, yet nearly all methods used to detect protein interactions from cells are qualitative. Here, we introduce a native holdup (nHU) approach that requires minimal sample preparation, investigates binding mechanisms, and quantifies equilibrium binding constants of protein interactions from cell extracts. We use nHU coupled with western blotting (nHU-WB) and mass spectrometry (nHU-MS) to explore interactions of SNX27, a cargo adaptor of the retromer complex. We find good agreement between affinities measured using minimal binding fragments and those measured directly from cell extracts using nHU. This challenges the unwritten paradigm stating that biophysical parameters like binding constants cannot be accurately determined from cells or cellular extracts. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

2023-03-11 | PXD034790 | Pride

ChEC-seq kinetics discriminates transcription factor binding sites by DNA sequence and shape in vivo

Project description:Chromatin endogenous cleavage (ChEC) uses fusion of a protein of interest to micrococcal nuclease (MNase) to target calcium-dependent cleavage to specific genomic loci in vivo. Here we report the combination of ChEC with high-throughput sequencing (ChEC-seq) to map budding yeast transcription factor (TF) binding. Temporal analysis of ChEC-seq data reveals two classes of sites for TFs, one displaying rapid cleavage at sites with robust consensus motifs and the second showing slow cleavage at largely unique sites with low-scoring motifs. Sites with high-scoring motifs also display asymmetric cleavage, indicating that ChEC-seq provides information on the directionality of TF-DNA interactions. Strikingly, similar DNA shape patterns are observed regardless of motif strength, indicating that the kinetics of ChEC-seq discriminates DNA recognition through sequence and/or shape. We propose that time-resolved ChEC-seq detects both high-affinity interactions of TFs with consensus motifs and sites preferentially sampled by TFs during diffusion and sliding.

2015-10-22 | GSE67453 | GEO

DNA mismatches reveal conformational penalties in protein-DNA recognition VI

Project description:Transcription factors (TF) recognize specific genomic sequences to regulate complex gene expression programs. Although it is well established that TFs bind specific DNA sequences using a combination of base readout and shape recognition, some fundamental aspects of protein-DNA binding remain poorly understood. Many DNA-binding proteins induce changes in the DNA structure outside the intrinsic B-DNA envelope. However, how the energetic cost associated with distorting DNA contributes to recognition has proven difficult to study because the distorted DNA exists in low-abundance in the unbound ensemble. Here, we use a novel high-throughput assay called SaMBA (Saturation Mismatch-Binding Assay) to investigate the role of DNA conformational penalties in TF-DNA recognition. In SaMBA, mismatches are introduced to pre-induce DNA structural distortions much larger than those induced by changes in Watson-Crick sequence. Strikingly, approximately 10% of mismatches increased TF binding, and at least one mismatch was found that increased the binding affinity for each of 22 examined TFs. Mismatches also converted non-specific sites into high-affinity sites, and high-affinity sites into super-sites stronger than any known canonical binding site. Determination of high-resolution X-ray structures, combined with NMR measurements and structural analyses revealed that many of the mismatches that increase binding induce distortions similar to those induced by protein binding, thus pre-paying some of the energetic cost to deform the DNA. Our work indicates that conformational penalties are a major determinant of protein-DNA recognition, and reveals mechanisms by which mismatches can recruit TFs and thus modulate replication and repair activities in the cell.

2020-08-29 | GSE157067 | GEO

DNA mismatches reveal conformational penalties in protein-DNA recognition I

2020-08-16 | GSE156304 | GEO

DNA mismatches reveal conformational penalties in protein-DNA recognition IV

2020-08-29 | GSE157065 | GEO

DNA mismatches reveal conformational penalties in protein-DNA recognition III

2020-08-29 | GSE157064 | GEO

DNA mismatches reveal conformational penalties in protein-DNA recognition II

2020-08-18 | GSE156373 | GEO

DNA mismatches reveal conformational penalties in protein-DNA recognition V

2020-08-29 | GSE157066 | GEO